How to Use the Ziflow MCP in Mastra AI

Q: How do I get proof details with Ziflow and Mastra AI?

You call getproof to retrieve the necessary details. This data then feeds into your workflow engine, allowing you to check against business rules before continuing the process.

Q: Does the MCP Server handle webhooks with Ziflow and Mastra AI?

Yes, listwebhooks shows active integrations. Your agent can then use this list to decide if a specific workflow needs to trigger or update an external endpoint.

Q: Can I search for proofs using Ziflow and Mastra AI?

You run searchproofs when you need to check the status of content. The agent processes this list, determining if further manual action or automated steps are needed.

Q: What is the data type touched by Ziflow and Mastra AI concerning contacts?

The server touches contact records via getcontactbyemail. You use this to confirm a user's identity or status within your workflow.

Q: Does the MCP Server allow listing proof folders with Ziflow and Mastra AI?

Yes, calling listfolders gives you an inventory of where proofs are stored. This helps your agent map out which resources need to be updated.

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Create your Vinkius account to connect Ziflow to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Ziflow with Mastra AI

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Handle Contact Lookups in Workflow Logic

Run `get_contact_by_email` to find a contact record. This data feeds directly into your workflow engine, allowing conditional branching based on whether the contact exists. If the check fails, your Mastra AI agent can automatically trigger retries or notify an admin, building true resilience into your process.

Manage Proof Decision Submission

`submit_decision` lets your multi-step workflow finalize a review. The decision is recorded and the flow moves to the next step—like creating a new proof via `create_proof`. This ensures that every action in your complex pipeline, from start to finish, follows strict business logic.

Monitor Proof Metadata Changes

Use `list_integration_properties` to check the metadata attached to proofs. This is crucial for workflows where you need to validate compliance or necessary inputs before proceeding. The agent can use this data point in a loop, checking multiple properties until all required conditions are met.

Setup guide

Set up Ziflow MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Ziflow tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "ziflow-mcp-client",
  servers: {
    "ziflow-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Ziflow Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Ziflow tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Ziflow transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ziflow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Ziflow MCP in Mastra AI

You call `get_proof` to retrieve the necessary details. This data then feeds into your workflow engine, allowing you to check against business rules before continuing the process.

Yes, `list_webhooks` shows active integrations. Your agent can then use this list to decide if a specific workflow needs to trigger or update an external endpoint.

You run `search_proofs` when you need to check the status of content. The agent processes this list, determining if further manual action or automated steps are needed.

The server touches contact records via `get_contact_by_email`. You use this to confirm a user's identity or status within your workflow.

Yes, calling `list_folders` gives you an inventory of where proofs are stored. This helps your agent map out which resources need to be updated.

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